Projects

A 'Digital Twin' of your company is a simulation model that behaves exactly the same as your company would at this moment in time. It allows you to do what-if analysis on operational decisions: what happens if I delay this customer order, what happens if I assign this work item to this department, what happens if this machine goes down for maintenance? We develop simulation models that are as close to reality as possible, by 'learning' them from company data. We specialize in doing so for supply chain processes and for administrative business processes, including financial and governmental services processes.

By forecasting the demand of your customers, you can better plan your supply chain and production. Modern demand forecasting techniques, based on AI, are better capable of learning complex demand patterns and to learn the relation of demand with other factors, such as the weather, promotional campaigns, and your order book. We develop such novel techniques for demand forecasting and demonstrate their applicability in projects together with companies.

Business models define the value an organization (or a network or organizations) offers to its customers, the capabilities required to deliver that value, and the associated costs and benefits. Digital business models that harness the power of digital technologies and network connectivity, such as platforms, have brought about disruptive changes in traditional industries, establishing themselves as key players in the digital economy (e.g., Uber, Airbnb, Amazon, and Spotify).  Trending socio-economic models, such as the sharing economy - shifting from ownership to the value of utilizing goods within specific contexts, and advances in digital technologies such as Internet-of-Things (IoT), blockchain, and AI are fueling the rapid pace of this disruption. We study and develop methods, tools, and techniques to support the design, implementation, operation, evaluation, monitoring & controlling, and continual improvement of digital business models and their alignment with operating models, including business processes and IT systems. We are particularly interested in collaborative digital business models (e.g., sharing economy platforms), where value co-creation takes place in a multi-stakeholder setting. 

The role of innovative business models stands as the cornerstone for organizations striving to harmonize environmental consciousness with economic success. This project focuses on guiding organizations toward embracing circularity and sustainability transition through effective business model design and implementation. By leveraging emerging technologies (e.g., IoT, blockchain) and strategies, we assist in designing and implementing sustainable practices that minimize waste, encourage recycling, and foster eco-friendly operations. Through this initiative, organizations gain the methods, tools, and insights needed to drive positive environmental and social impact while achieving economic growth in alignment with circular and sustainable principles.

Despite the rising importance of advanced data analytics (ADA), there is limited guidance on how organizations should leverage it. The benefits that an organization can gain through advanced data analytics depend on the organization’s ability to gain and use relevant capabilities. Such capabilities are diverse, ranging from technical ones to those related to an organization's ability to define its ADA strategy, manage relevant processes, bring together the right talents, and define collective values. Management needs guidance on how to implement and improve these capabilities. Without a clearly defined roadmap for improvement, organizations will face difficulties in achieving consensus on the priorities and order of improvement activities. In close collaboration with industry experts, we have developed models and guidelines to help organizations assess and improve their capabilities for managing advanced data analytics. Such models support organizations to self-assess their advanced data analytics capabilities, identify where they excel, see areas that need improvements, and create a roadmap for improving these capabilities.

Is your company seeking to proactively detect and predict abnormal behaviors in your assets? Examples of such anomalies could include machine failures and last-mile logistics parcel losses. Predicting these anomalies can be a challenging task for conventional machine learning methods. However, our expertise lies in crafting custom deep learning models and algorithms, tailored to deliver accurate predictions, with techniques from transformer, attention, graph neural network, transfer learning,  to name a few. 

Is your company facing complex decision-making dilemmas that demand smart algorithms to optimize objectives such as cost, efficiency, or workload fairness? These challenges are hard to be solved using traditional algorithms as they often involve uncertainties, dynamic factors, large-scale sizes, and numerous constraints. We are experts in developing novel AI-driven solutions to tackling these hurdles, leveraging cutting-edge techniques like Deep Reinforcement Learning, Surrogate Modeling, and Deep Hybrid Solvers. Our AI-driven solutions find applications across various domains, from optimizing routes in transportation, scheduling jobs on machines in manufacturing, to enhancing order batching and picking operations in warehousing.

Is your company struggling with a blackbox prediction model that's challenging to extract meaningful insights from? The insights derived from prediction models can be invaluable for comprehending and enhancing your current business operations. We have shown the potential of Explainable AI (XAI) to demonstrate how XAI can play a pivotal role in enhancing business processes and enriching the overall value proposition in a case study with e-commerce retailers. Furthermore, with XAI, we can empower communication between humans and AI, facilitating collaborative creation of personalized, trustworthy AI decision support tool.